The transportation capacity of urban environments serves as an integral function in modern society. Many transportation infrastructures are stretched to their limits as, every day, an increasing number of people travel on highways. The Transportation Demand Management (TDM) industry seeks to provide a solution to congested thoroughfares by using existing roads more efficiently through the adjustment of commuter travel behavior. This approach to building new roads has the added advantages of reducing congestion, air pollution, and fossil fuel consumption as well as providing mobility solutions for non-drivers.
To provide effective solutions for increased demand, TDM strategies require an in-depth understanding of the local area's transportation patterns. In the past, transportation experts have relied on self-reporting paper and/or phone surveys to analyze trip characteristics including start and end times, duration, distance, origin, destination, purpose, mode, etc. However, these types of surveys have several intrinsic problems. First, the amount of time and effort required to complete an accurate travel survey is significant. As a result, recruiting participants is challenging; often the length of the study must be limited to one or two days to avoid placing an undue burden on respondents. Second, the desired level of detail and accuracy are impeded by self-reporting user errors, apathy, and intentional or unintentional omissions, particularly on short trips. Finally, once the surveys are collected, they must be manually post-processed, thus requiring a significant amount of time and effort.
In recent years, modern computing devices including Global Positioning System (GPS)-enabled mobile phones have been evaluated as possible replacements for paper and phone surveys. GPS-enabled mobile phones can be carried by the user whenever and wherever he or she travels, and provide the opportunity for recording an individual's transportation behavior for any mode of transportation, including travel via public transit, or non-motorized modes such as walking or biking. The objective nature of GPS data, combined with the automated data collection process, can enhance the quality and quantity of collected data. Mobile phones are also capable of transferring GPS data to a central database immediately upon collection, which allows for extended deployment of the survey. Real-time data connectivity also introduces new services to the traveler such as highly targeted traffic alerts based on the user's real-time location and predicted destination. These services help to reduce traffic congestion while providing the user an incentive to allow their travel behavior to be monitored.
One of the most important travel characteristics obtained from GPS data is the user's route (i.e., the path that he or she takes from source to destination). Although route information can be easily obtained without the user's intervention by continuously calculating and sending GPS fixes from the cell phone to a server, this method will quickly drain energy from the cell phone's battery. When tracking for an entire day is desired, power consumption becomes a significant concern that can render a mobile phone inoperable if resources are not managed properly by the application. Furthermore, frequent transmission of unnecessary information not only increases the cost of the user's phone bill, but also utilizes additional network resources. To solve these problems while retaining the ability to continue tracking the user and reconstructing the user's path, new dynamic application-level location management algorithms are required.
In terminal-based location methods in which the mobile device is the primary position-calculating entity, the device location must be sent to a server to update the system with real-time position information. Many methods for sending mobile device positioning updates to a server have been discussed in existing literature, including polling, zone updates, distance updates, and dead reckoning.
In polling, a server pulls the position from the mobile device on a periodic basis, or as requested by the server-side location-based application. Polling is useful for occasional position updates, such as displaying the current location of devices on a map, but is not efficient for real-time applications with on-board intelligence that relies upon real-time position information, or for detailed route recording. Periodic updates are sent from the mobile device to the server after a fixed interval of time elapses. While it is one of the most commonly used update methods, a significant amount of unnecessary data can be sent to the server when small interval values are used. Additionally, the specific fixed interval must be customized per application. While large time intervals are more efficient, they do not meet the needs of real-time or archival applications.
The mobile device can also send position updates based on zones and distance. In zone-based methods, the mobile device sends GPS fixes when it enters or leaves a particular geographic zone. Distance-based methods trigger a position update after the mobile phone has exceeded a distance threshold. These two methods must also be customized with a set interval per application, and tend to send more GPS fixes than necessary. Distance-based methods also send unnecessary location updates when the user is traveling in a straight line.
Dead reckoning is another method that determines whether to send a new GPS fix based on the most recent location data that was sent to the server, and an estimation function executed simultaneously on the device and the server. For example, if the device detects that its position deviates from what is expected when the estimation function is executed with the most recent server data, it then sends the new position to the server. While this method results in fewer transmissions to the server, it requires the continuous execution of potentially costly estimation functions on both the mobile device and the server. For significantly resource-constrained devices, such as those that meet the qualifications of the Java ME Connected Limited Device Configuration (CLDC), these estimation functions may consume significant resources and may not be feasible for real-time operation.
For tracking applications that run in the background on a mobile phone for an extended period of time, none of the above-described positioning update methods provide an efficient means to deliver both real-time Location-Based Services (LBS), as well as an accurate record of the user's travel path within the same application. Therefore, to achieve highly efficient LBS, a new method that dynamically adjusts at run-time is needed.
Many location-based applications are used to track users with Global Positioning System (GPS)-enabled mobile devices in real-time. However, the continuous calculation and transmission of GPS fixes from the mobile device to the main server consumes a considerable amount of energy and increases data transmission costs.